U.S. patent application number 14/525451 was filed with the patent office on 2015-04-30 for two stage seismic velocity model generation.
This patent application is currently assigned to BP CORPORATION NORTH AMERICA INC.. The applicant listed for this patent is Andrew Brenders, Joseph Anthony Dellinger. Invention is credited to Andrew Brenders, Joseph Anthony Dellinger.
Application Number | 20150120200 14/525451 |
Document ID | / |
Family ID | 51871315 |
Filed Date | 2015-04-30 |
United States Patent
Application |
20150120200 |
Kind Code |
A1 |
Brenders; Andrew ; et
al. |
April 30, 2015 |
TWO STAGE SEISMIC VELOCITY MODEL GENERATION
Abstract
A computer-implemented process includes: performing a first foil
waveform inversion on an initial subsurface attribute model using
low frequency, known source-signature data and low frequency
humming seismic data to generate a first updated subsurface
attribute model; and performing a second full waveform inversion on
the first updated subsurface attribute model using low-frequency,
narrowband sweeping known source-signature data and low-frequency,
swept seismic data to generate a second updated subsurface
attribute model. The process may be performed by a suitably
programmed computing apparatus, the program residing on some form
of non-transitory program storage medium.
Inventors: |
Brenders; Andrew;
(Naperville, IL) ; Dellinger; Joseph Anthony;
(Naperville, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Brenders; Andrew
Dellinger; Joseph Anthony |
Naperville
Naperville |
IL
IL |
US
US |
|
|
Assignee: |
BP CORPORATION NORTH AMERICA
INC.
Houston
TX
|
Family ID: |
51871315 |
Appl. No.: |
14/525451 |
Filed: |
October 28, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61896394 |
Oct 28, 2013 |
|
|
|
Current U.S.
Class: |
702/18 |
Current CPC
Class: |
G01V 1/303 20130101;
G01V 1/005 20130101; G01V 2210/6222 20130101 |
Class at
Publication: |
702/18 |
International
Class: |
G01V 1/28 20060101
G01V001/28 |
Claims
1. A computer-implemented process, comprising: performing a first
roll waveform inversion on an initial subsurface attribute model
using low frequency, known source-signature data and low frequency
humming seismic data to generate a first updated subsurface
attribute model; and performing a second full waveform inversion on
the first updated subsurface attribute model using low-frequency,
narrowband sweeping known source-signature data and low-frequency,
swept seismic data to generate a second updated subsurface
attribute model.
2. The computer-implemented process of claim 1, wherein the first
full waveform inversion comprises a frequency domain full waveform
inversion.
3. The computer-implemented process of claim 2, wherein the
frequency domain full waveform inversion includes time-domain
finite-difference modeling.
4. The computer-implemented process of claim 2, wherein the second
full waveform inversion comprises a time domain full waveform
inversion.
5. The computer-implemented process of claim 1, wherein the second
full waveform inversion comprises a time domain full waveform
inversion.
6. The computer-implemented process of claim 1, wherein the
low-frequency, humming seismic data includes data acquired at a
seismic frequency of less than about 4 Hz.
7. The computer-implemented process of claim 6, wherein the
low-frequency, humming seismic data includes data acquired at a
seismic frequency of less than about 2 Hz.
8. The computer-implemented process of claim 6, wherein the
low-frequency, humming seismic data includes data acquired at a
seismic frequency of less than about 1.5 Hz.
9. The computer-implemented process of claim 1, wherein the
low-frequency, known source signature, humming seismic data
comprises less than 10 frequencies.
10. The computer-implemented process of claim 1, wherein the
low-frequency, narrowband sweeping known source-signature seismic
data are acquired at between about 2 Hz and about 8 Hz.
11. The computer-implemented process of claim 1, wherein the
low-frequency, narrowband sweeping known source-signature data are
acquired at between about 1.5 Hz and about 6 Hz.
12. The computer-implemented process of claim 1, wherein the first
full waveform inversion omits true source signature
determination.
13. The computer-implemented process of claim 1, wherein the first
updated subsurface attribute model comprises recovered
low-frequency information.
14. The computer-implemented process of claim 1, wherein the second
full waveform inversion omits true source signature
determination.
15. The computer-implemented process of claim 1, wherein the second
updated subsurface attribute model comprises both low-wavenumber
and high-wavenumber information.
16. The computer-implemented process of claim 1, wherein the
low-frequency, known source signature, humming seismic data and the
low-frequency, narrowband, known source signature, swept seismic
data include common frequencies.
17. The computer-implemented process of claim 1, wherein performing
the second full waveform inversion using low-frequency, narrowband
sweeping known source-signature data includes performing the second
full waveform inversion using the physical record of the
low-frequency, narrowband sweeping known source-signature data.
18. The computer-implemented process of claim 1, wherein performing
the second full waveform inversion using low-frequency, narrowband
sweeping known source-signature data and low frequency humming
seismic data includes performing the second full waveform inversion
using a single complex-valued scalar, representing the phase and
amplitude of the humming source.
19. A computing apparatus, comprising: a processor; a communication
medium; a storage; and a software component residing on storage
that, when executed by the processor over the communication medium,
performs a method including: performing a first full waveform
inversion on an initial subsurface attribute model using low
frequency, known source-signature data and low frequency humming
seismic data to generate a first updated subsurface attribute
model; and performing a second lull waveform inversion on the first
updated subsurface attribute model using low-frequency, narrowband
sweeping known source-signature data and low-frequency, swept
seismic data to generate a second updated subsurface attribute
model
20. The computing apparatus of claim 19, wherein the first full
waveform inversion comprises a frequency domain full waveform
inversion.
21. The computing apparatus of claim 19, wherein the second full
waveform inversion comprises a time domain full waveform
inversion.
22. The computing apparatus of claim 19, wherein the low-frequency,
humming seismic data includes data acquired at a seismic frequency
of less than about 4 Hz.
23. The computing apparatus of claim 19, wherein the low-frequency,
narrowband sweeping known source-signature data are acquired at
between about 1.5 Hz and about 6 Hz.
24. A non-transitory program storage medium, encoded with
instructions that, when executed, perform a computer-implemented
method, the method comprising: performing a first full waveform
inversion on an initial subsurface attribute model using low
frequency, known source-signature data and low frequency humming
seismic data to generate a first updated subsurface attribute
model; and performing a second full waveform inversion on the first
updated subsurface attribute model using low-frequency, narrowband
sweeping known source-signature data and low-frequency, swept
seismic data to generate a second updated subsurface attribute
model.
25. The non-transitory program storage medium of claim 24, wherein
the first full waveform inversion comprises a frequency domain full
waveform inversion.
26. The non-transitory program storage medium of claim 24, wherein
the second full waveform inversion comprises a time domain lull
waveform inversion.
27. The non-transitory program storage medium of claim 24, wherein
the low-frequency, humming seismic data includes data acquired at a
seismic frequency of less than about 4 Hz.
28. The non-transitory program storage medium of claim 24, wherein
the low-frequency, narrowband sweeping known source-signature data
are acquired at between about 1.5 Hz and about 6 Hz.
Description
RELATED APPLICATION
[0001] This application is related to and claims priority from U.S.
Provisional Application No. 61/896,394 entitled "Two Stage Seismic
Velocity Model Generation," filed Oct. 28, 2013, in the name of the
inventors Andrew Brenders and Joseph Anthony Dellinger, the entire
contents of which are hereby fully incorporated herein by reference
for all purposes.
FIELD OF THE INVENTION
[0002] The presently disclosed technique pertains to the processing
and analysis of seismic data for the location of subsurface
hydrocarbons and other fluids and, more particularly, to the
generation of seismic velocity models for use in such
activities.
BACKGROUND OF THE INVENTION
[0003] The pursuit of hydrocarbons and some other fluids is
sometimes greatly hampered by their being located in deposits
underground, in certain, types of geological formations. Such
deposits most be identified and located by indirect, rather than
direct, observation. This typically involves imparting acoustic
waves of certain frequencies into the ground. When they encounter
certain features in geological formations, they are reflected back
to the surface and recorded as seismic data. The seismic data
contains information regarding the buried geological formations
from which one can ascertain things like the presence and location
of hydrocarbon deposits. That is, seismic data are representative
of the geological formations from which they are obtained.
[0004] For example, one tool frequently used in the analysis of the
seismic data is what is known as a "velocity model". A velocity
model is a representation of the geological formation that can be
used in analysis. It may be used to, for example, convert the
seismic data into one or more "seismic domains" that image the
geological formation in different ways. The quality of these images
frequently depends upon the quality of the velocity model. It may
also be used in other ways to, for another example, analyze various
geophysical characteristics of the formation. Other types of models
of the underlying geological formations, collectively called
"subsurface attribute models" herein, are also used and implicate
similar considerations.
[0005] Over time, the need to locate hydrocarbon deposits more
accurately and more precisely has grown. Sometimes advances in
accuracy and precision come in the form of new acquisition
techniques. Other times such advances are achieved through the
manner in which the seismic data are processed such as those
described in the above. Sometimes advances result from a
combination of developments in both acquisition and processing.
[0006] The use of low frequencies for imaging in general, and for
generating subsurface attribute models in particular, has proven
challenging for frequencies below about 6 Hz, particularly for
frequencies below about 4 Hz. The challenge is twofold: at lower
frequencies the natural background noise of the Earth gets
progressively stronger, and conventional broadband sources such as
airguns get progressively weaker. As a result, the signal-to-noise
of deepwater marine seismic data can decline at over 20 dB per
octave for frequencies below 4 Hz.
[0007] Thus, while there may be many suitable techniques for
seismic imaging in general and for generating subsurface attribute
models in particular, the need for increased effective
signal-to-noise at low frequencies continues to drive innovation in
the art. In particular, among other things, there is a need for
acquisition and processing techniques that enhance our ability to
acquire and use low-frequency seismic data at lower frequencies.
The art is therefore receptive to improvements or at least
alternative means, methods and configurations that might further
the efforts at improvement. The art will consequently well receive
the technique described herein.
SUMMARY
[0008] In a first aspect, a computer-implemented process includes:
performing a first full waveform inversion on an initial subsurface
attribute model using low frequency, known source-signature data
and low frequency humming seismic data to generate a first updated
subsurface attribute model; and performing a second full waveform
inversion on the first updated subsurface attribute model using
low-frequency, narrowband sweeping known source-signature data and
low-frequency, swept seismic data to generate a second updated
subsurface attribute model.
[0009] In a second aspect a computing apparatus is programmed to
perform the process.
[0010] In a third aspect, a non-transitory program storage medium
is encoded with instructions that, when executed by a computing
apparatus, perform the process.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate embodiments of
the invention and together with the description, serve to explain
the principles of the invention. In the figures;
[0012] FIG. 1 and FIG. 2 conceptually illustrate the work flow of a
two-stage process for generating a seismic subsurface attribute
model in accordance with one particular embodiment of the presently
disclosed technique.
[0013] FIG. 3 depicts selected portions of the hardware and
software architecture of an exemplary computing apparatus on which
that aspect of the presently disclosed technique shown in FIG. 1
and FIG. 2 may be performed.
[0014] FIG. 4 depicts an exemplary acquisition for the seismic data
in one particular embodiment.
[0015] FIG. 5 illustrates one particular embodiment of the
computing apparatus of FIG. 3 which is, more particularly, a
computing system on which some aspects of the present invention may
be practiced in some embodiments.
[0016] FIG. 6 depicts a synthetic true model used to illustrate the
operation, of one particular embodiment.
[0017] FIG. 7 illustrates one particular embodiment of the workflow
first shown in FIG. 1.
[0018] FIG. 8 depicts an initial velocity model first shown in FIG.
7.
[0019] FIG. 9 graphs the source signature in the time domain for
the low frequency humming data acquired as shown in FIG. 4 and used
in the exemplary workflow of FIG. 7.
[0020] FIG. 10 shows the phase of the data in FIG. 9 for all
sources and receivers.
[0021] FIG. 11 depicts the first updated velocity model of the
embodiment in FIG. 7 upon completion of the selected number of full
waveform inversion ("FWI") iterations.
[0022] FIG. 12-FIG. 13 present a one-dimensional slice through the
first updated velocity model in FIG. 11, with the starting model
and true model for comparison.
[0023] FIG. 14 graphs the source signature in the time domain for
the narrowband swept data of the embodiment in FIG. 7.
[0024] FIG. 15 depicts the second updated velocity model of the
embodiment in FIG. 7 upon completion of the selected number of FWI
iterations.
[0025] FIG. 16 presents a one-dimensional slice through the second
updated velocity model in FIG. 15, with the first updated velocity
model and true model for comparison.
DESCRIPTION OF THE EMBODIMENTS
[0026] Accurate subsurface attribute models provide good seismic
images of the subsurface. The presently disclosed technique is a
method for improving acquisition, processing, and in particular
subsurface attribute model building in an environment where the
signal-to-noise ratio rapidly decreases at progressively lower
frequencies. We define "low frequencies" as "frequencies below
which getting sufficient signal-to-noise with conventional airgun
sources rapidly becomes more difficult as the frequency decreases",
i.e., below about 6-8 Hz.
[0027] One strategy for acquiring data as described above uses a
controlled-frequency source to generate a narrow-bandwidth signal.
This concentrates the source's power into a narrower frequency
bandwidth, thereby increasing the signal-to-noise over that narrow
bandwidth. In general "narrow bandwidth" means less than two
octaves, although in some embodiments it may be up to three
octaves. See U.S. application Ser. No. 13/327,524, filed Dec. 15,
2011.
[0028] Particularly useful in producing a narrow bandwidth is a
source that can also produce a single monochromatic frequency. In
this case, the bandwidth is limited by the frequency stability of
the source, the length of time the source is active, or the length
of time that can be considered as a single "shot point," given the
wavelengths of the signal and the speed of motion of the source
relative to the acquisition grid. Acquisition with a monochromatic
controlled-frequency source in this manner is "humming acquisition"
and a source operated in this way a "humming source".
[0029] If the sources were unlimited in strength, one could simply
use a source loud enough to overcome the noise background. One
could then use conventional broadband processing/inversion/imaging
techniques for low frequencies just as is already done for higher
frequencies where the Earth's noise background is not so
challenging. In practice the size and power of our sources are
limited by practical, technical, and environmental considerations.
As a result, conventional processing has had limited success at low
frequencies. Nevertheless, it is still desirable to use sources
that are "as broadband as practicable".
[0030] With data of sufficiently low frequencies and
signal-to-noise, one technique well known to those in the art for
constructing a subsurface attribute model is full-waveform
inversion ("FWI"). Typically, FWI begins at low frequencies (as low
as possible) and then adds higher and higher frequencies. The
subsurface attribute model thereby slowly comes into focus with
progressively finer features being added as the rounds of inversion
continue. The subsurface attribute model output by each stage of
the process then becomes the starting model for the next stage. See
L. Sirgue & R. G. Pratt, "Efficient Waveform Inversion and
Imaging: A Strategy for Selecting Temporal Frequencies", 69
Geophysics 231 (2004) ("Sirgue & Pratt (2004)").
[0031] The presently disclosed technique provides two stages of a
three-stage process for generating an improved seismic subsurface
attribute model. A first stage uses humming acquisition at the
lowest frequencies, where the signal-to-noise challenge is
greatest. The technique then transitions to narrowband acquisition
at somewhat higher (but still low) frequencies, taking advantage of
the increasing signal-to-noise to allow an increased (but still
narrowband) source bandwidth. Finally, at higher frequencies, where
the signal-to-noise ratio of the data is no longer limiting, we
transition to conventional broadband acquisition and processing, as
is well known in the art. This third stage corresponds to current
practice and will not be discussed further.
[0032] Reference will now be made in detail to the exemplary
embodiments of the subject matter claimed below, examples of which
are illustrated in the accompanying drawings. Wherever possible,
the same reference numbers will be used throughout the drawings to
refer to the same or like parts.
[0033] FIG. 1 and FIG. 2 conceptually illustrate a work flow 100 of
a two-stage process for generating a subsurface attribute model in
accordance with one particular embodiment of the presently
disclosed technique. In the illustrated embodiment of the presently
disclosed technique, the subsurface attribute model is a seismic
velocity model. In some embodiments, the subsurface attribute may
be an isotropic parameter such as velocity, density, bulk modulus,
or shear modulus. In other embodiments, the subsurface parameter
may be an anisotropic parameter such as epsilon, delta, or the
constants of the stillness tensor. The subsurface attribute model
may also comprise two or more parameters at each spatial location,
for example velocity and density.
[0034] The work flow 100 begins with an initial subsurface
attribute model 110 of the geological formation for which the
seismic data being processed has been acquired. The subsurface
attribute modeled by the subsurface attribute model 110 may be
either an isotropic or an anisotropic attribute. In this particular
embodiment, the subsurface attribute is seismic velocity. The
initial velocity model 110 may be of any kind generated by any
technique known to those in the art. This may include, for example,
a velocity model generated by reflection tomography although it may
be as simple as a one-dimensional ("1D") velocity gradient.
[0035] The initial velocity model 110 may be developed from data
acquired in the survey whose results are being analyzed. It may
therefore be generated specifically as the starting point for the
two stage process described herein. However, in some embodiments,
the initial subsurface attribute model 110 may be a "legacy model"
of an earlier analysis or generated from "legacy data" acquired in
an earlier survey of the geological formation under analysis. The
technique admits wide latitude in the generation and selection of
the initial subsurface attribute model 110.
[0036] The work flow 100 then performs (at 200, FIG. 2) a first FWI
120 on the initial subsurface attribute model 110 using
low-frequency, known source-signature data and humming seismic data
125 to generate a first updated subsurface attribute model 130. As
is well known to those of ordinary skill in the art, the
transmission, reflection, diffraction, etc., of seismic waves
within the earth can be modeled with considerable accuracy by the
wave equation, and accordingly wave-equation-based
wavefleld-extrapolation engines are the method of choice for
difficult imaging problems. The wave equation is a partial
differential equation that can readily be couched in terms of one,
two, or three dimensions.
[0037] For complex imaging challenges, the constant-density
acoustic wave equation extrapolating in time is typically used as
the extrapolation engine. Coupled with an imaging condition it
yields an image of reflectors inside the earth. Imaging in this way
is called "reverse-time migration". The same extrapolation engine
can also be used within an iterative optimization process that
attempts to find an earth model that explains all of the seismic
information recorded at the receivers. This is called
"full-waveform inversion". Ideally, inversion produces a
three-dimensional ("3D") volume giving an estimated subsurface wave
velocity at each illuminated point within the earth. If the
acoustic wave equation is used, which incorporates both velocity
and density as medium parameters, inversion may produce a
3-dimensional volume giving both the velocity and density at each
point.
[0038] Returning to FIG. 1, the first updated subsurface attribute
model 130 may model either an isotropic or an anisotropic
attribute. Depending on the embodiment, the first FWI 120 may be
either a time-domain or a frequency-domain FWI. Still other
embodiments may find other kinds of FWI suitable for
implementation. Those in the art having the benefit of this
disclosure will appreciate that the FWI is an iterative process, as
indicated by the broken line 135. If the first FWI 120 is a
time-domain implementation, the known source signature will be
input as a time-series. If the first FWI 120 is a frequency-domain
implementation, the known source signature will be input as a
single complex-valued scalar, representing the phase and amplitude
of the humming source, or in some embodiments just the phase.
[0039] As mentioned above, the FWI is performed using the low
frequency, known source-signature data and "humming" seismic data,
i.e., the data 125 ("DATA.sub.1"). In an embodiment, "low
frequency" is less than, about 6-8 Hz and, more typically, less
than about 4 Hz. In another embodiment, the low-frequency humming
seismic data includes data acquired at a seismic frequency of less
than about 2 Hz. In yet another embodiment, the low-frequency
humming seismic data includes data acquired at a seismic frequency
of less than, about 1.5 Hz. The term "about" is a recognition that
in acquisition seismic sources may come out of calibration or be
poorly calibrated, simultaneously radiate at additional frequencies
(for example from harmonics or from noise from a compressor), or
that their signals can drift or in other ways deviate from what is
desired. Thus, the term "about" means that the actual frequency is
within the operational error acceptable to those in the art
relative to the desired frequency of acquisition.
[0040] Also as is mentioned above, the source signature of the
seismic data is known. This particular embodiment therefore omits
true source signature determination in this FWI. Those in the art
will appreciate that the source signature permits the analysis to
identify certain characteristics defining the conditions under
which the source signal is imparted into the environment. These
include characteristics such as the location, depth, and velocity
of the source, the hum produced by the source, and more generally
the complete time history (phase, amplitude, or both) of the
radiated acoustic signal for each hum, as are well known in the
art. In some embodiments, use of the source signature in this
manner will include use of the physical record. In other,
alternative embodiments, it may involve representing the source
signature in a single complex-valued scalar number in a manner
known to the art.
[0041] The seismic data 125 is also known as "humming" seismic
data. The term "humming" identifies the mode of acquisition.
"Humming" is using a non-impulsive controlled-frequency source that
generates substantially all of its energy at a single frequency.
Due to practical stability limitations the source may instead
perform a controlled or uncontrolled drift within a narrow
frequency range, typically staying within plus or minus one tenth
of an octave around the nominal frequency. This is sometimes what
is called "monochromatic" or "near monochromatic", for example m
U.S. application Ser. No. 13/327,524.
[0042] Humming acquisition may occur in several different ways. For
example, stepped humming is a sequential humming acquisition in
which a single source steps over a set of two or more discrete
frequencies, one at a time. The time spent moving between
frequencies should be very small compared to the time spent at each
frequency. Chord humming is a humming acquisition in which two or
more sources simultaneously hum at differing discrete frequencies.
More information is available in U.S. application Ser. No.
13/327,524.
[0043] When humming acquisition is performed at differing discrete
frequencies, the first stage may be iterated for a number of
low-frequency humming seismic datasets, each acquired with
monotonically increasing low-frequency humming sources. The
subsurface attribute model from the FWI of the previous humming
source would be used as the initial subsurface attribute model for
the FWI of the next low-frequency humming seismic dataset, with the
frequencies of each dataset increasing monotonically. In this
particular embodiment, the first stage is described as being
performed once, for a single low-frequency humming dataset. In
other embodiments, the first stage may be performed two or more
times, for a number of low-frequency humming seismic datasets at
different frequencies, as indicated by the optional outer iteration
loop 205. A typical number of humming datasets may be 2, and
probably not exceeding 10. Returning to FIG. 1-FIG. 2, similarly,
the second stage may also be performed two or more times for a
number of different narrowband sweeping seismic datasets, as
indicated by the optional outer iteration loop 215.
[0044] Returning to FIG. 1-FIG. 2, the first stage yields the first
updated subsurface attribute model 130. The first FWI 120 will
typically involve an inner iteration loop, as indicated by dotted
line 135. The first updated subsurface attribute model 130 has
several advantages as a starting point for further model generation
relative to conventional practice. Among these are that it includes
the low-frequency portion of the subsurface attribute model and
that the true source signature is known and was used in its
generation. Both of these arise from the nature of the seismic data
125.
[0045] The first updated subsurface attribute model 130 is then
used as the starting point for the second stage of the presently
disclosed process. The second stage performs (at 210, FIG. 2) a
second FWI 140 on the first updated subsurface attribute model 130
using a narrowband sweeping known source signature and swept
seismic data 145 to generate a second updated subsurface attribute
model 150. The second updated subsurface attribute model 150 may
model either an isotropic or an anisotropic attribute. Again, for
each dataset the second FWI 140 is typically an iterative process,
as indicated by the dashed line 155. The second FWI 140 may be the
same kind of inversion (time-domain or frequency-domain) as the
first FWI 120 or may be different depending on the embodiment.
[0046] The seismic data 145 is similar to the seismic data 125 in
that its source signature is known. This particular embodiment
therefore also omits true source signature determination in this
FWI. However, the seismic data 145 differs from the seismic data
125 in that it was acquired by sweeping rather than humming.
Sweeping typically involves acquisition using a non-impulsive
controlled-frequency source that starts producing sound at one
frequency and then smoothly transitions to a second frequency
before stopping. Typically the device would then reset, pause, and
then begin a new sweep. Consecutive sweeps may be identical (the
usual case) or different. The sweep may be either up (the usual
case) or down in frequency. In the illustrated embodiment, the
starting and ending frequencies typically will differ by up to two
octaves, but sweeps over narrower frequency ranges are also
possible. Alternative embodiments may sweep across up to three
octaves. In an embodiment, the low-frequency, narrowband sweeping
known source-signature data are acquired at between about 1.5 Hz
and about 6 Hz.
[0047] Swept seismic data can be classed in at least two types. One
is "narrowband sweeping", in which acquisition uses sweeps covering
a frequency range of two octaves or less. A. second is "broadband
sweeping", which is acquisition using sweeps covering a frequency
range of more than two octaves. Conventional vibroseis-style
acquisition, as is well known in the art, uses broadband sweeping.
The presently disclosed technique, however, uses narrowband
sweeping. One narrowband swept acquisition technique suitable for
obtaining data used the present technique is disclosed in U.S.
application Ser. No. 13/327,524.
[0048] Those in the art will appreciate that seismic data
acquisition occurs in seismic surveys that are sometimes classified
by the environment in which they are performed. One type of
acquisition is known as "marine" seismic surveying, which occurs in
aquatic environments including not only saltwater, but also fresh
and brackish water. A second type is known as "land based" or
"land" surveying and occurs on land. The third kind may be called a
"transition zone" survey, which occurs in environments partially on
land and partially on water. The presently disclosed technique is
not limited by whether the seismic data 125, 145 are acquired using
a marine, land based, or transitional zone survey. The seismic data
125, 145 may be acquired using any such type of survey.
[0049] Those in the art will appreciate that seismic data itself is
sometimes described as one-dimensional ("1D"), two-dimensional
("2D"), or three-dimensional ("3D") depending on the design of the
apparatus by which the seismic data are acquired. (The design
affects the subterranean coverage of the survey so that it is, for
example, 1D, 2D, or 3D.) There is also a four-dimensional ("4D")
seismic data type in which 3D data are taken in at least two
different surveys separated in time, time being the fourth
dimension. The embodiments illustrated herein are applied to 3D
data but the disclosed technique is equally applicable to 1D, 2D,
and 4D seismic data.
[0050] Note that the seismic data 125, 145 and the first and second
updated subsurface attribute models 130, 150 are collections of
ordered data representative of a tangible, real world, natural
environment. This includes tangible, real world objects that
comprise that environment. The seismic data 125, 145 and the first
and second updated subsurface attribute models 130, 150 may, or may
not be, rendered for human perception either by electronic display
or by hard copy reduction depending upon the particular embodiment
being implemented. The disclosed technique is indifferent as to
whether such a rendering occurs. The seismic data 125, 145 and the
first and second updated subsurface attribute models 130, 150 in
the illustrated embodiments are not rendered but are instead
analyzed without rendering.
[0051] Those in the art having the benefit of this disclosure will
also appreciate that the aspect of the presently disclosed
technique described above is computer-implemented. FIG. 3
conceptually depicts selected portions of the hardware and software
architecture of a computing apparatus 300 such as may be employed
in some aspects of the present invention. The computing apparatus
300 includes a processor 303 communicating with storage 306 over a
communication medium 309.
[0052] The processor 303 may be any suitable processor or processor
set known to the art. Those in the art will appreciate that some
types of processors will be preferred in various embodiments
depending on familiar implementation-specific details. Factors such
as processing power, speed, cost, and power consumption are
commonly encountered in the design process and will be highly
implementation specific. Because of their ubiquity in the art, such
factors will be easily reconciled by those skilled in the art
having the benefit of this disclosure. Those in the art having the
benefit of this disclosure will therefore appreciate that the
processor 303 may theoretically be an electronic micro-controller,
an electronic controller, an electronic microprocessor, an
electronic processor set, or an appropriately programmed
application specific integrated circuit ("ASIC"), field
programmable gate array ("FPGA"), or graphical processing units
("GPUs"). Some embodiments may even, use some combination of these
processor types.
[0053] However, those in the art will also appreciate data sets
such as the seismic data 125, 145 are quite voluminous and that the
processing described herein is computationally intensive. Typical
implementations for the processor 303 therefore actually constitute
multiple electronic processor sets spread across multiple computing
apparatuses working in concert. One such embodiment is discussed
below. These considerations affect the implementation of the
storage 306 and the communication medium 309 similarly.
[0054] The storage 306 may include a hard disk and/or random access
memory ("RAM") and/or removable storage such as a floppy magnetic
disk 312 and an optical disk 315. The storage 306 is encoded with a
number of software components. These components include an
operating system ("OS") 318; an application 321; data structures
324, 327 including the seismic data 125 ("DATA.sub.1"), 145
("DATA.sub.2"); and the first ("FUM") and second ("SUM") updated
subsurface attribute models 130, 150. The storage 306 may be
distributed across multiple computing apparatuses as described
above.
[0055] As with the processor 303, implementation-specific design
constraints may influence the design of the storage 306 in any
particular embodiment. For example, as noted above, the disclosed
technique operates on voluminous data sets which will typically
mitigate for various types of "mass" storage such as a redundant
array of independent disks ("RAID"). Other types of mass storage
are known to the art and may also be used in addition to or in lieu
of a RAID. As with the processor 303, these kinds of factors are
commonplace in the design process and those skilled in the art
having the benefit of this disclosure will be able to readily
balance them in light of their implementation specific design
constraints.
[0056] The processor 303 operates under the control of the OS 318
and executes the application 321 over the communication medium 309.
This process may be initiated automatically, for example upon
startup, or upon user command. User command may be directly through
a user interface. To that end, the computing system 300 of the
illustrated embodiment also employs a user interface 342.
[0057] The user interface 342 includes user interface software
("UIS") 345 and a display 340. It may also include peripheral
input/output ("I/O") devices such as a keypad or keyboard 350, a
mouse 355, or a joystick 360. These will be implementation-specific
details that are not germane to the presently disclosed technique.
For example, some embodiments may forego peripheral I/O devices if
the display 340 includes a touch screen. Accordingly, the presently
disclosed technique admits wide variation in this aspect of the
computing system 300 and any conventional implementation known to
the art may be used.
[0058] Furthermore, there is no requirement that the functionality
of the computing system 300 described above be implemented as
disclosed. For example, the application 321 may be implemented in
some other kind of software component, such as a daemon or utility.
The functionality of the application 321 need not be aggregated
into a single component and may be distributed across two or more
components. Similarly, the data structures 324, 327 may be
implemented using any suitable data structure known to the art.
[0059] As with the processor 303 and the storage 306, the
implementation of the communications medium 309 will vary with the
implementation. If the computing system 300 is deployed on a single
computing apparatus, the communications medium 309 may be, for
example, the bus system of that single computing apparatus. Or, if
the computing system 300 is implemented across a plurality of
networked computing apparatuses, then the communications medium 309
may include a wired or wireless link between the computing
apparatuses. Thus, the implementation of the communications medium
309 will be highly dependent on the particular embodiment in ways
that will be apparent to those skilled in the art having the
benefit of this disclosure.
[0060] Some portions of the detailed descriptions herein are
presented in terms of a software implemented process involving
symbolic representations of operations on data bits within memory
in a computing system or a computing device. These descriptions and
representations are the means used by those in the art to most
effectively convey the substance of their work to others skilled in
the art. The process and operation require physical manipulations
of physical quantities that will physically transform the
particular machine or system on which the manipulations are
performed or on which the results are stored. Usually, though not
necessarily, these quantities take the form of electrical,
magnetic, or optical signals capable of being stored, transferred,
combined, compared, and otherwise manipulated. It has proven
convenient at times, principally for reasons of common usage, to
refer to these signals as bits, values, elements, symbols,
characters, terms, numbers, or the like.
[0061] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated, or otherwise as may be
apparent, throughout the present disclosure, these descriptions
refer to the action and processes of an electronic device, that
manipulates and transforms data represented as physical
(electronic, magnetic, or optical) quantities within some
electronic device's storage into other data similarly represented
as physical quantities within the storage, or in transmission or
display devices. Exemplary of the terms denoting such a description
are, without limitation, the terms "processing, " "computing,"
"calculating," "determining," "displaying," and the like.
[0062] Furthermore, the execution of the software's functionality
transforms the computing apparatus on which it is performed. For
example, acquisition of data will physically alter the content of
the storage, as will subsequent processing of that data. The
physical alteration is a "physical transformation" in that it
changes the physical state of the storage for the computing
apparatus.
[0063] Note also that the software implemented aspects of the
invention are typically encoded on some form of program storage
medium or, alternatively, implemented over some type of
transmission medium. The program storage medium may be magnetic
(e.g., a floppy disk or a hard drive) or optical (e.g., a compact
disk read only memory, or "CD ROM"), and may be read only or random
access. Similarly, the transmission medium may be twisted wire
pairs, coaxial cable, optical fiber, or some other suitable
transmission medium known to the art. The invention is not limited
by these aspects of any given implementation.
[0064] Those in the art will appreciate that the two stage process
for generating a seismic subsurface attribute model is a part of a
larger process stretching from acquisition of the seismic data 125,
145 through its pre-processing and processing to the analysis of
the processed data. To further an understanding of the presently
disclosed technique, the two stage process for generating a seismic
subsurface attribute model will now be disclosed in an embodiment
in which it is in fact just such a part of a larger process. Note,
however, that in the discussion of the processing below, synthetic
data rather than real world data are used.
[0065] As mentioned above, one suitable acquisition technique is
disclosed in U.S. application Ser. No. 13/327,524. Portions of that
application will now be reproduced with some modification in order
to further an understanding of this technique. One such
modification is the substitution of the term "humming" as set forth
above for the terms "monochromatic" and "near monochromatic".
However, other acquisition techniques may be employed in other
embodiments provided they result in the acquisition of humming and
or narrowband sweeping seismic data such as is described above.
[0066] FIG. 4 illustrates a marine acquisition geometry suitable
for implementing the instant invention. In some embodiments, a
seismic survey will be conducted in the ocean 400 over a subsurface
target of geological interest 426 which lies beneath the seafloor
425. A vessel 410 floats on the ocean surface 420. In the survey
system, the vessel 410 may tow one or more low-frequency humming
and/or narrowband sweeping sources 450, each of which will contain
a receiver or sensor (not shown) that will record the wavefield
emitted by that source. These comprise the "narrowband,
low-frequency" portion of one embodiment of the instant survey
system.
[0067] In one particular embodiment, the humming or narrowband
source is implemented using the source disclosed and claimed in
U.S. Pat. No. 8,387,744, incorporated by reference below. However,
the technique is not limited to acquisition with this particular
source. Alternative embodiments may utilize other sources so long
as they are capable of use in acquiring humming and narrowband
swept data as described above.
[0068] The low-frequency sources 450 are shown towed at deeper
depths; in some embodiments each will be towed at a depth
appropriate for its frequency range, such that the surface ghost
reflection maximally enhances the downward-propagating signal.
Thus, the deeper the depth of tow, the lower the frequency of the
humming or narrowband swept source. See, for example, U.S.
application Ser. No. 12/291,221 or U.S. Pat. No. 7,257,049, which
discuss the relationship between depth and frequency of
acquisition. For some types of sources, the available frequency
range shifts upwards with increasing depth, for example because an
increase in water pressure raises the resonant frequency of the
source. Thus, in other embodiments the lower-frequency sources will
be towed at shallower depths, despite the attenuation from the
surface ghost reflection that this may cause.
[0069] Many variations of this acquisition system are possible and
well within the ability of one of ordinary skill in the art to
devise. The instant survey system could acquire 2D, 3D, or 4D data.
Variations in the design of the spread or the number of vessels
will also be readily appreciated by those skilled in the art having
the benefit of this disclosure. The low-frequency narrowband survey
could be performed at the same time as the conventional,
higher-frequency broadband survey, or in a separate pass, or in
multiple separate passes. Alternatively, a low-frequency narrowband
survey could be used to supplement a previously acquired
conventional higher-frequency broadband survey such that the
original data are re-processed with the additional low-frequency
data, or a low-frequency narrowband survey could be acquired first,
and a conventional higher-frequency broadband survey later.
[0070] The low-frequency sources 450 could operate continuously.
The low-frequency sources could each operate at a single frequency
or cycle between two or more discrete frequencies ("humming"
low-frequency sources), or sweep over a narrowband range of low
frequencies designed to augment the frequency range produced by the
broadband sources ("narrowband sweeping" low-frequency sources).
The sources could operate to produce waves of constant amplitude,
or the amplitude of the waves could vary (taper up and down).
[0071] The one or more low-frequency humming datasets, one or more
narrowband sweeping datasets, and conventional broadband datasets
may be acquired in any order. In particular, they may be acquired
sequentially, or interleaved by shot lines, or interleaved within a
shot line, or acquired simultaneously and separated using any of
the standard techniques known in the art, or in any combination of
these. One or more of the datasets may be "legacy" data, acquired
previously for other purposes.
[0072] Note that, for purposes of illustration only, the discussion
herein is primarily directed toward the design of a marine survey.
That being said, those of ordinary skill in the art having the
benefit of this disclosure will readily understand how the instant
approach might be modified for a survey to be taken on land or in a
transition zone.
[0073] The principles of conventional survey design with airguns
are well established and will not be discussed here. Full-waveform
inversion is generally described in terms of inverting data for a
narrow range of frequencies, or over a set of discrete frequencies,
from low frequency to high frequency. One algorithm for selecting
frequencies is given in Sirgue & Pratt (2004). This reference
suggests frequencies should be selected that are separated by a
ratio of about 2 for this survey geometry. In one approach, a
theoretically optimal ratio between successive frequencies can be
shown to be equal to:
1 .alpha. min 1 + ( o 2 d ) 2 ##EQU00001##
where o is the maximum offset and d is the depth of the target of
interest. So, for example, consider a maximum offset of 20
kilometers and a target depth of interest of 6 kilometers.
Then:
1 .alpha. min 34 9 .apprxeq. 1.94 ##EQU00002##
[0074] So, starting from 1.4 Hz and following the prescription
identified above, the next frequency would be 2.72 Hz, followed by
5.28 Hz etc. The last frequency is likely within the range
available from conventional sources such as airguns, so in this
case only 2 frequencies would be used from a controlled-frequency
source: 1.4 and 2.72 Hz. In this example the next frequency would
be 5.28 Hz, but that frequency will, be available from the data
collected using the conventional broadband sources, so a
low-frequency source may not be used to acquire data of that
frequency. In practice it might be desirable to be a bit
conservative and acquire more frequencies below those available
from airguns, but this example still illustrates that only a few
frequencies may be needed for realistic examples.
[0075] Thus in the instant example, two or more humming sources may
be used, operating at 1.4 and 2.72 Hz, respectively. In other
embodiments, a single source simultaneously humming at a
fundamental and a second harmonic, 1.4 and 2.8 Hz, might be used,
or a single source might alternate back and forth between 1.4 and
2.72 Hz. Returning to FIG. 1-FIG. 2, these two humming seismic
datasets would each be used in Stage 1 of the process, in
successive order of increasing frequency (at 205), with the updated
subsurface attribute model from the first FWI of the 1.4 Hz data
being used, as the initial subsurface attribute model for the FWI
of the 2.72 Hz data.
[0076] The previous paragraphs describe humming acquisition.
Narrowband sweeping acquisition is closer to conventional
controlled-source acquisition, the primary difference being that in
narrowband sweeping acquisition we do not attempt to sweep over a
sufficient bandwidth to make an interpretable seismic image from
the resulting data. The data are instead optimized to provide a
sufficient signal-to-noise ratio for full-waveform inversion. So,
for example, we might sweep over 2-8 Hz, two octaves. The minimum
acceptable bandwidth for an interpretable image is about 3
octaves.
[0077] It may further be desirable to choose to perturb the
frequencies of the humming sources to prevent unwanted interference
of harmonics between the seismic sources. For example, if the
theory suggests that sources emitting waves 1.0 and 2.0 Hz should
be employed, it might be preferred instead to use 0.9 and 2.1 Hz,
to avoid having one source frequency conflict with the second
harmonic of the other. Optionally the harmonic or subharmonic
output of a humming or narrowband source might, be enhanced, and
use made of the harmonics or subharmonics as additional humming
sources. So, for example, one source might simultaneously generate
waves having frequencies of 1.4 and 2.8 Hz.
[0078] In this particular embodiment, a joint survey is conducted
although some embodiments may separate the broadband and
low-frequency, narrowband surveys. The conventional survey may
proceed in accordance with conventional practice. If the airguns
emit acoustic energy with a detectable intensity at, for example,
2.8 Hz, the highest of the low-frequency sources, it might be
desirable to slightly modify the timing of each shot so that the
2.8 Hz wave component of the airgun signal is timed to be in-phase
with the waves produced by the 2.8 Hz low-frequency source(s). Note
at most this would require delaying or advancing the shot timing by
1.4 seconds. Alternatively, the vessel speed could be adjusted so
that the airguns reach their shot locations just at the desired
point in the humming source's cycling. Note the energy of the
acoustic signal produced from airguns rapidly falls off at lower
frequencies, so any unwanted interference will be much reduced for
any lower low-frequency sources.
[0079] The narrowband low-frequency sources may operate
independently or simultaneously. The narrowband low-frequency
sources may operate continuously or discontinuously. Each
narrowband low-frequency source records the signal it is radiating,
and this information will be used when performing the full-waveform
inversion. The receivers could be recording continuously. The
locations of all sources and receivers will in some embodiments,
also be recorded continuously.
[0080] Turning now from the acquisition and the excerpted material,
the humming and narrowband swept data are recorded during
acquisition and transported to a computing facility in conventional
fashion. This typically includes recording the seismic data on some
kind of electromagnetic medium, such as a magnetic tape 460 or a
digital memory (not shown). The magnetic tape 460 may be
transported by ground transportation (not shown), for example, to a
computing facility 470. Or, the seismic data may be transmitted, by
satellite 480.
[0081] The computing facility 470 typically houses a more powerful
computing system than what may be found on the vessel 410. The
situs of the processing described herein is immaterial. In theory,
the processing may be performed on the vessel 410 or, for that
matter, anywhere else. However, the processing is computationally
intensive and so more powerful computing systems than are typically
found on survey vessels are generally desirable.
[0082] A portion of an exemplary computing system 500 is shown in
FIG. 5. The computing system 500 is networked, but there is no
requirement that the computing system 500 be networked. Alternative
embodiments may employ, for instance, a peer-to-peer architecture
or some hybrid of a peer-to-peer and client/server architecture.
The size and geographic scope of the computing system 500 is not
material to the practice of the invention. The size and scope may
range anywhere from just a few machines of a Local Area Network
("LAN") located in the same room to many hundreds or thousands of
machines globally distributed in an enterprise computing
system.
[0083] The computing system 500 comprises, in the illustrated
portion, a server 510, a mass storage device 520, and a workstation
530. Each of these components may be implemented in their hardware
in conventional fashion. Alternative embodiments may also vary in
the computing apparatuses used to implement the computing system
500. Those in the art will furthermore appreciate that the
computing system 500, and even that portion of it that is shown,
will be much more complex. However, such detail is conventional and
shall not be shown or discussed to avoid obscuring the subject
matter claimed below.
[0084] In FIG. 5, the application 321 is shown residing on the
server 510 while the data structures 324, 327 for the seismic data
125, 145, and the subsurface attribute models 130, 150 are shown
residing in the mass storage 520. While this is one way to locate
the various software components, the technique is not dependent
upon such an arrangement. Although performance concerns may
mitigate for certain locations in particular embodiments, the situs
of the software components is otherwise immaterial.
[0085] The operation of this particular embodiment will be
illustrated in the context of synthetic data. The synthetic data
are derived from the 2D synthetic model 600 of a geological
formation shown in FIG. 6. The model is indexed by distance (X)
measured in meters across the horizontal (x) axis and by depth (Z)
in meters along the vertical (y) axis. Note the circular
inhomogeneity in the center. The velocity bar 620 is shown to the
right in accordance with conventional practice.
[0086] The elliptical, high-velocity anomaly 610 is 1500 m thick,
centered at a depth of 5000 m, embedded in a 1D background velocity
gradient that increases from a constant 1500 m/s in a water layer
at the top of the model to 5500 m/s at the base. The model is
dtscretized on a grid with Dx=Dz=100 m, and spans a distance of 46
100 m laterally and 10 000 m vertically. The 18 triangles across
the top indicate the approximate acquisition geometry of the
experiment, simulating 422 ocean bottom receivers regularly spaced
every 100 m over the model, located at a depth of 1500 m. The
sources were simulated every 100 m, towed at a depth of 30 m below
the lop of the model.
[0087] The user 540 invokes the application 321 from the
workstation 530 to perform the particular workflow 700 shown in
FIG. 7. Those in the art will appreciate that the seismic data 125,
145 may undergo pre-processing to condition the data for the
processing that is to come. Such pre-processing is described in,
for example, U.S. Pat. No. 7,725,266 and U.S. application Ser. No.
13/327,524. The type and amount of pre-processing will vary by
embodiment in a manner that will become apparent to those skilled
in the art having the benefit of this disclosure.
[0088] The first stage begins with a 1D velocity model 710 upon
which, for the true low-frequency humming source-signature data
712, recorded by or near the source, and the humming data 711,
recorded at the receivers, the workflow 700 begins by performing
the full waveform inversion 720 in the frequency-domain
("FWI.sub.f") for a number of discrete frequencies. In the
illustrated embodiment, the humming data 711 are acquired with a
frequency of less than about 2 Hz--i.e., 1.51 Hz. By using
known-source humming data 711 at these low-frequencies, one of the
problems associated with FWI is solved and another mitigated: the
source signature 712 is known, and the starting velocity model does
not have to be extremely accurate doe to the presence of
low-frequency data to mitigate the nonlinearity of the inverse
problem.
[0089] The initial velocity model 710 is first derived through some
other method. It may be a legacy model or it may be derived
expressly for purposes of performing the disclosed technique. This
"other" method by which it is derived will typically be reflection
tomography, though it could even be as simple as a 1D velocity
gradient. The initial velocity model 710 in the illustrated
embodiment is shown in FIG. 8 and is a 1D velocity model.
[0090] The seismic data by which the initial velocity model 710 is
updated as described above are generated by a device operating in a
monofrequency "humming" mode, generating a known source signature
for a small number (<<10) of low-frequencies. The source
signature for a synthetic set of humming data generated from the
synthetic model 600 in FIG. 6 at a frequency of 1.51 Hz is shown in
FIG. 9. FIG. 10 shows the phase of the humming data generated in
the true velocity model of FIG. 6 for all sources and receivers.
Note the elliptical shape of the phase patterns near the center of
the figure--this is due to the presence of the elliptical velocity
anomaly 610, and represents the data that the full waveform
inversion is trying to match.
[0091] Synthetic data were calculated in this model using both a
1.51 Hz "humming" sweep and as discussed below a narrowband
sweeping source containing frequencies from 2 to 8 Hz. Data were
modeled using the pseudo-analytic approximation to the acoustic
wave-equation, as described by J. T. Etgen & S. Brandberg-Dahl,
"The Pseudo-Analytical Method: Application of Pseudo-Laplacians to
Acoustic and Acoustic Anisotropic Wave Propagation", 79nd Annual
international Meeting, SEG, Expanded Abstracts, 2552-2556 (2009)
and U.S. Pat. No. 8,296,069 issued Oct. 23, 2012. The data were
modeled using a free-surface boundary condition, and recorded for a
maximum time of 65 s. Data were also modeled with the narrowband
sweeping source and recorded for a maximum time of 56 s.
[0092] The FWI.sub.f 720 is performed using the technique disclosed
in U.S. Pat. No. 7,725,266, which is similar to that in Sirgue
& Pratt (2004). This technique employs a multi-scale approach.
That is, it decomposes the seismic data 125 by scale--with the
larger scale data, typically represented by the lower data
frequencies--being much easier to match in the non-linear,
iterative inverse problem of updating a velocity model. The
technique gradually matches different components of the seismic
data 125--moving from easiest to hardest, largest to smallest,
gradually increasing the resolution of our seismic velocity
models.
[0093] Using the starting velocity model 710, and seismic data 711
of a single, low-frequency, as described above, the workflow 700
runs multiple iterations 735 of frequency-domain waveform inversion
("FWI.sub.f") 720 to update the velocity model 710. Typically, the
number of frequencies ranges from 1 to less than 10. In the
illustrated embodiment, this FWI.sub.f 720 is performed using the
time-domain finite-difference forward modeling disclosed in U.S.
Pat. No. 7,725,266.
[0094] The first updated velocity model 740 for this particular
embodiment is shown in FIG. 11 upon completion of the iterations
735. The first stage of the processing flow described above
operates in the frequency-domain. In this particular embodiment,
FWI.sub.f is parameterized to invert for only the phase of the
monofrequency, or humming, data. FIG. 11 is the result after 10
iterations of FWI.sub.f. Although there is some variation in the 1D
background velocity model, which is not expected to change very
much, the FWI.sub.f result has succeeded in recovering a
low-frequency estimate of the elliptical velocity anomaly in the
center of the model. In other embodiments, the updated model 740
would then be used as the initial model 710 for additional
FWI.sub.f 720 of several other (<<10) low-frequency humming
source signatures 712 and low-frequency humming data 711.
[0095] The result from the first stage of the processing flow just
described is further illustrated by FIG. 12 and FIG. 13, which
present a 1D slice through the velocity model at X=23.0 km,
approximately through the center of both the model and the anomaly.
In FIG. 12, the true model 600 and the result 1200 after one
iteration of FWI.sub.f is shown. The starting model 710 mirrors the
result 1200 and so is not separately shown. In FIG. 13, result 740
after 10 iterations of FWI.sub.f, (representing a low-pass filtered
version of the true model 600), the true model 600, and the
starting model 710 are shown. Frequency-domain waveform inversion
of "humming" data at a low-frequency (<4 Hz) has allowed
FWI.sub.f to recover a velocity model which, while not exactly the
true model, would not have been recoverable from the same starting
model if data of a higher temporal frequency were used (i.e., >5
Hz, typical of airgun seismic data).
[0096] At this point the illustrated embodiment of the disclosed
technique departs from conventional practice that would employ
further iterations of the FWI.sub.f 720, extracting from
conventional data the discrete frequencies of interest. Here, as in
the first stage, the presently disclosed technique further exploits
the attribute of the data as described above in which the output
source signature is known. Using a low-frequency, narrowband
sweeping, known source-signature, and swept seismic data, this
particular embodiment performs a further full-waveform inversion in
the time-domain, iteratively updating the velocity model without
having to invert for the source signature.
[0097] Waveform inversion in the time-domain (i.e., "FWIt") is
essentially inverting for multiple frequencies simultaneously, as
described by A. Brenders, A., et al., "Comparison of 3D Time- and
Frequency-Domain Waveform Inversion: Benefits and Insights of a
Broadband, Discrete-Frequency Strategy", SEG Technical Program
Expanded Abstracts 2012: pp. 1-5 (2012) ("Brenders, et al.").
However, waveform inversion in the time-domain still requires
starting velocity models which avoid the local minima associated
with our non-linear inverse problem. Using "humming" data to create
a starting model by FWI.sub.f implicitly implements a multi-scale
strategy to mitigate the aforementioned noniinearity. Incorporating
the measured source signature into the processing eliminates one of
the key difficulties in implementing FWI.sub.t in that it
eliminates the need to estimate the source signature of the
data.
[0098] Returning to FIG. 7, the first updated velocity model 740 is
then used to perform the second stage processing. The second
dataset 750 is narrowband sweeping data acquired at frequencies
from, for example, 2 Hz to 8 Hz, recorded in the time domain. The
source signature 745 for the data used in this particular
embodiment is shown in FIG. 14. The time-domain, narrowband
sweeping data 750 recorded at the receivers along with its measured
source signature 745 are then used with the first updated velocity
model 740 through FWI.sub.t 755 to generate the second updated
velocity model 760 over a number of iterations 765.
[0099] The FWI.sub.t 755 can be performed as described in Brenders
et al. However, the technique is not so limited and other FWI.sub.t
techniques known to the art may be used. Other suitable techniques
include A. Pica, et al., "Nonlinear Inversion of Seismic Reflection
Data in a Laterally Invariant Medium", 55 Geophysics 284-292
(1990); R. M. Shipp & S. C Singh, "Two-Dimensional Full
Wavefield Inversion of Wide-Aperture Marine Seismic Streamer Data:
151 Geophys. J. Int. 325-344 (2002).
[0100] The second updated velocity model 760 resulting from the
FWI.sub.t 755 after seven iterations 765 is shown in FIG. 15. The
variation in the 1D background velocity model, has been mostly
"healed" by the inversion, and the elliptical velocity anomaly at
the center of the model has been better recovered, especially at
the top and lateral edges. The "sharpening" of the velocity anomaly
is an effect of the FWI.sub.t procedure. The FWI.sub.t effectively
inverts for a limited bandwidth of frequencies simultaneously, and
by adding all of these frequencies to the inverted velocity model,
the technique smooths out the "ringing" effect in the velocity
model which is representative of the single-frequency approach used
by our frequency-domain waveform inversion, algorithm, as described
by U.S. Pat. No. 7,725,266.
[0101] The result from the second stage is shown in FIG. 16. This
presents a 1D slice through the velocity model at X=23.0 km,
approximately through the center of both the model and the anomaly.
In FIG. 16, the true model 600, the first updated velocity model
740 (the result after ten iterations of FWI.sub.f), and the result
1600 after ten iterations of FWI.sub.t, the second updated model,
are shown. The time-domain waveform inversion has both resulted in
a more accurate recovery of both the top and bottom edge of the
anomaly, as well as the total magnitude (value) of the velocity
model itself. This velocity model, while not necessarily
appropriate for imaging (i.e., migration) of seismic data acquired
with airguns, represents a much better starting model for further
velocity analysis, whether by additional waveform inversion of
higher frequency (i.e., airgun) data, or by standard methods of
velocity model building for high-velocity anomalies.
[0102] The technique disclosed herein addresses one of the
uncertainties in applying FWI with standard seismic data in
conventional practice--that the seismic source signature is an
unknown variable. As an unknown variable, it must be solved for as
part of the inverse problem in conventional practice. In addition,
both the source and seismic data do not typically contain
sufficient low-frequencies (<4 Hz) for FWI to succeed without a
good knowledge, a priori, of the subsurface velocity model. When
applying FWI with low-frequency, known source seismic data as
described above, these problems can be overcome or at least
mitigated.
[0103] Furthermore, as mentioned above, the FWI.sub.t 755 in FIG. 7
is essentially inverting for a wider band of frequencies
simultaneously, as described in Brenders et al. This is true
because (1) the low-frequency portion of the velocity model has
already been recovered by using FWI.sub.f with "humming" data, and
(2) the true source signature used to generate our seismic data is
known. By iteratively updating the velocity model without having to
invert for the source signature, and due to the quality and
accuracy of the starting model coming from first stage of the
processing flow, this technique recovers velocity models with both
low-wavenumber and high-wavenumber information simultaneously.
[0104] The following patent applications and patents are hereby
incorporated by reference for those portions that are listed and
for the purposes set forth as if set forth verbatim herein.
[0105] U.S. application Ser. No. 13/327,524, entitled, "Seismic
Acquisition Using Narrowband Seismic Sources", filed Dec. 15, 2011,
in the name of the inventors Joseph A. Dellinger et al., published
Jun. 21, 2012, as U.S. Patent Publication 2012/0155217, and
commonly assigned herewith for its teachings regarding data
acquisition located at [0024]-[0040], [0054]-[0059],
[0065]-[0088].
[0106] U.S. Pat. No. 7,725,266, entitled, "System and Method for 3D
Frequency Domain Waveform Inversion Based on 3D Time-Domain Forward
Modeling", and issued May 25, 2010, to BP Corporation North.
America Inc., as assignee of the inventors Laurent Sirgue et al.,
for its teaching regarding the full waveform inversion technique at
column 7, line 64 to column 13, line 50, with reference to FIGS.
3-5.
[0107] U.S. Pat. No. 8,387,744, entitled, "Marine Seismic Source",
and issued Mar. 5, 2013, to BP Corporation North America Inc., as
assignee of the inventors Mark Harper et al., for its teaching
regarding the design and operation of a humming and narrowband
seismic source at column 5, line 62 to col. 12, lines 46.
[0108] The following papers are hereby incorporated by reference
for those portions that are listed and for the purposes set forth
as if set forth verbatim herein.
[0109] A. Brenders, et al., "Comparison of 3D Time- and
Frequency-Domain Waveform Inversion: Benefits and Insights of a
Broadband, Discrete-Frequency Strategy", SEG Technical Program
Expanded Abstracts 2012: pp. 1-5 (2012).
[0110] L. Sirgue & R. G. Pratt, "Efficient Waveform Inversion
and Imaging: A Strategy for Selecting Temporal Frequencies", 69
Geophysics 231 (2004), for its teachings regarding full waveform
inversion and, in particular, frequency selection found at pages
232-246.
[0111] A. Pica, et al., "Nonlinear Inversion of Seismic Reflection
Data in a Laterally Invariant Medium", 55 Geophysics 284-292
(1990).
[0112] R. M. Shipp & S. C. Singh, "Two-Dimensional Full
Wavefield inversion of Wide-Aperture Marine Seismic Streamer Data:
151 Geophys. J. Int. 325-344 (2002).
[0113] To the extent that any patent, patent application or paper
incorporated by reference herein conflicts with the present
disclosure, the present disclosure controls.
[0114] Other embodiments of the invention will be apparent to those
skilled in the art from consideration of the specification and
practice of the invention disclosed herein. It is intended that the
specification and examples be considered as exemplary only, with a
true scope and spirit of the invention being indicated by the
following claims.
* * * * *